## Alpha level

Also known as the alpha risk. It’s the acceptable risk of committing a Type A error, or incorrectly rejecting your null hypothesis. Alpha level is always a number between 0 and 1—most commonly, people use a value of 0.05. Once your test is complete and you’ve run the collected data through statistical software, you’ll have a p-value to compare to your alpha level.

## Alternative hypothesis

A hypothesis that disagrees with the null hypothesis; the two are mutually exclusive.

## Beta level

Also known as the beta risk. It’s the acceptable risk of committing a Type B error – ie, not rejecting your null hypothesis when it is, in fact, incorrect.

## Conclusion

A statement which indicates the level of evidence (sufficient or insufficient), at what level of significance, and whether the original claim is rejected (null) or supported (alternative).

## Confidence level

Also known as the confidence interval. This refers to how confident you can be that your conclusion is in fact correct. The confidence level is easy to calculate: the alpha and confidence levels always add up to one. ie:

1 – α = confidence level

## Critical region

Set of all values which would cause us to reject the null hypothesis. Also known as a rejection region.

## Critical value(s)

The value(s) which separate the critical region from the non-critical region. The critical values are determined independently of the sample statistics.

A critical value separates the rejection region from the non-rejection region.

## Decision

A statement based upon the null hypothesis. It is either “reject the null hypothesis” or “fail to reject the null hypothesis”. We will never accept the null hypothesis.

## Error

Two basic types of error occur in hypothesis testing: type A errors, where a correct hypothesis is rejected; and type B errors, where an incorrect hypothesis is accepted. Read more about errors.

## H0

Also known as the null hypothesis.

## H1

Also known as the alternative hypothesis, or H(a).

## Left-tailed test

If the alternative hypothesis H1 contains the less-than inequality symbol (<), the hypothesis test is a left-tailed test.

## Null hypothesis

The statement that you’re trying to disprove. Generally, this is the assumption that the experimental results are due to chance alone; nothing else influenced the results.

## P-value

A p-value is a crucial element of any hypothesis test results. It’s a number between 0 and 1, and it gauges the probability that random fluctuations caused any data that might cause you to reject the null hypothesis. It’s calculated by running test results through a statistical significance test. If the p-value is lower than your alpha level, then you reject the null hypothesis. If higher, then you do not reject the null hypothesis. Read more about p-values.

## Rejection region

Also known as a critical region.

## Right-tailed test

If the alternative hypothesis H1 contains the greater-than inequality symbol (>), the hypothesis test is a right-tailed test.

## Significance level

Also known as the alpha level.

## Two Tailed Test

A two-tailed test is one with two rejection regions. If the null hypothesis has an equal sign, then this is a two-tailed test and you can use the test statistic to reject the null hypothesis if the test statistic is too large or too small.

H0: µnew = µcurrent

Ha: µnew ≠ µcurrent

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